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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis
|
|---|---|
| Published in |
BMC Bioinformatics, February 2020
|
| DOI | 10.1186/s12859-020-3401-5 |
| Pubmed ID | |
| Authors |
Eugene Lin, Sudipto Mukherjee, Sreeram Kannan |
X Demographics
The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Czechia | 2 | 15% |
| Turkey | 1 | 8% |
| India | 1 | 8% |
| Italy | 1 | 8% |
| United Kingdom | 1 | 8% |
| Unknown | 7 | 54% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 54% |
| Members of the public | 6 | 46% |
Mendeley readers
The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 119 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 20% |
| Researcher | 17 | 14% |
| Student > Doctoral Student | 10 | 8% |
| Student > Master | 10 | 8% |
| Student > Bachelor | 6 | 5% |
| Other | 14 | 12% |
| Unknown | 38 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 23 | 19% |
| Biochemistry, Genetics and Molecular Biology | 13 | 11% |
| Engineering | 7 | 6% |
| Agricultural and Biological Sciences | 6 | 5% |
| Medicine and Dentistry | 5 | 4% |
| Other | 20 | 17% |
| Unknown | 45 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 26. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2023.
All research outputs
#1,424,804
of 25,008,338 outputs
Outputs from BMC Bioinformatics
#191
of 7,632 outputs
Outputs of similar age
#32,577
of 367,304 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 111 outputs
Altmetric has tracked 25,008,338 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,632 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 367,304 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.